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Found 62 Skills
Migrate an application with hardcoded LLM prompts to a full LaunchDarkly AgentControl implementation in five stages: audit the code, wrap the call, move the tools, add tracking, attach evaluators. Use when the user wants to externalize model/prompt configuration, move from direct provider calls (OpenAI, Anthropic, Bedrock, Gemini, Strands) to a managed config, or stage a full hardcoded-to-LaunchDarkly migration.
Resolve `/flag` style requests into the right LaunchDarkly flag lookup flow. Use when the user types `/flag`, asks to quickly find a flag by name/key, wants a direct flag detail summary, or needs fast disambiguation between similar flags.
Launchdarkly's UI design system. Use when building interfaces inspired by Launchdarkly's aesthetic - dark mode, Inter font, 4px grid.
Launch Darkly integration. Manage Segments, Projects, Users. Use when the user wants to interact with Launch Darkly data.
LaunchDay platform help — curated 24-hour product launch events for indie makers by Dagobert Renouf. Covers $99 submission process (pay only if selected), event model (1-2/month, max 20 products), deal requirements (>40% discount), 30-min podcast interview, influencer promotion (100K+ followers), Slack community access, and permanent archive. Use when unsure if LaunchDay is worth the $99, want more exposure for your indie product but free directories aren't enough, or wondering how LaunchDay compares to other paid options. Do NOT use for multi-directory launch strategy (use /sales-launch-directory). Do NOT use for Product Hunt launches (use /product-hunt-launch).
Guide for setting up AI configuration in your application. Helps you choose between agent vs completion mode, select the right approach for your stack, and create AI Configs that make sense for your use case.
Guide for giving your AI agents capabilities through tools. Helps you identify what your AI needs to do, create tool definitions, and attach them in a way that makes sense for your framework.
Create and manage agent graphs — directed graphs of AI Configs connected by edges with handoff logic. Use when building multi-agent workflows where configs route to each other.
Create a boolean first flag, add evaluation, toggle on/off for end-to-end proof. Parent onboarding Step 6; uses MCP, API, or ldcli; optional flag-create skill.
Experiment with configs by creating and managing variations. Helps you test different models, prompts, and parameters to find what works best through systematic experimentation.
Create, track, retrieve, update, and delete custom business metrics for AI Configs. Covers full lifecycle: define metric kinds via API, emit events via SDK, and query results.
Create and manage prompt snippets — reusable text blocks referenced inside AI Config variation prompts. Keeps common instructions, personas, and guardrails consistent across multiple configs.